There are plenty of problems that are easy for humans to solve, but are almost impossibly difficult for computers. Even though it seems that with modern computing power being what it is we should be able to solve a lot of these problems, things like identifying objects in images remains fairly difficult. Similarly, identifying specific sounds within audio samples remains problematic, and as [Eivind] found, is holding up a lot of medical research to boot. To solve one specific problem he created a system for counting coughs of medical patients.
This was built with the idea of helping people with chronic obstructive pulmonary disease (COPD). Most of the existing methods for studying the disease and treating patients with it involves manually counting the number of coughs on an audio recording. While there are some software solutions to this problem to save some time, this device seeks to identify coughs in real time as they happen. It does this by training a model using tinyML to identify coughs and reject cough-like sounds. Everything runs on an Arduino Nano with BLE for communication.
While the only data the model has been trained on are sounds from [Eivind], the existing prototypes do seem to show promise. With more sound data this could be a powerful tool for patients with this disease. And, even though this uses machine learning on a small platform, we have seen before that Arudinos are plenty capable of being effective machine learning solutions with the right tools on board.
Source: WEARABLE SENSOR TRAINED TO COUNT COUGHS
